Evidence map›Paper›PMID 38695875›Full record

ArticleSkeletal radiology2024

Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

John R Zech, Chimere O Ezuma, Shreya Patel, Collin R Edwards, Russell Posner, Erin Hannon, Faith Williams, Sonali V Lala, Zohaib Y Ahmad, Matthew P Moy and 1 more

Abstract read
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In one paragraph

Article in Skeletal radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 5 pooled it
3.5field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 5 syntheses or guidelines pooled it, 29 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

John R ZechDepartment of Radiology, New York University Langone Health, 301 E 17th St, New York, NY, 10003, USA. jrzech@gmail.com.
Chimere O EzumaDepartment of Orthopedic Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Shreya PatelDepartment of Radiology, New York University Langone Health, 301 E 17th St, New York, NY, 10003, USA.
Collin R EdwardsDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Russell PosnerDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Erin HannonDepartment of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
Faith WilliamsDepartment of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
Sonali V LalaDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Zohaib Y AhmadDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Matthew P MoyDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Tony T WongDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Columbia University Irving Medical Center · USNYU Langone Health · USUniversity of Southern California · US

Funding

RSNA Research and Education Foundation RR2216
6 · The paper itself

Abstract

purposeWe wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. MATERIALS AND

methodsA set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0-22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3-4 weeks later with AI assistance and recorded if/where fracture was present.

resultsAccess to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730-0.806] without AI to 0.876 [0.845-0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659-0.753] without AI to 0.844 [0.805-0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832-0.902] to 0.890 [0.856-0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030).

conclusionAn openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures.

Indexed as

Artificial IntelligenceFractures, BoneInternship and ResidencyAdolescentAlgorithmsChildChild, PreschoolClinical CompetenceFemaleHumansInfantInfant, NewbornMalePediatricsRadiographic Image Interpretation, Computer-AssistedRadiologyArtificial intelligenceDeep learningFractureMachine learningPediatricsUpper extremity

Identifiers

PMID38695875
OpenAlexW4396577846

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.